Message/Author 

Paul Spin posted on Friday, December 01, 2017  3:19 pm



A relative newbie to Mplus... I am attempting to model Y= B0 + B1*LAS + B2*Z + error where Y is a count variable, LAS is a latent class variable w/ 3 categories, and Z is a vector of covariates. I am following Web Notes 21 [Section 3.2], which returns classspecific intercepts and slope for each Z. Instead, I would like to get classindependent main effects for Z while still allowing for classspecific intercepts. Here is my code: TITLE: STAGE 1: Estimate latent class model; DATA: File = data.csv ; VARIABLE: NAMES = y a1a8 z; USEVARIABLES = a1a8 ; CATEGORICAL = a1a8; CLASSES = c(3); AUXILIARY = y z; ANALYSIS: TYPE = MIXTURE; Savedata: File = lcaoutput.csv ; Save = bchweights; TITLE: Y on C and Z ; DATA: File = lcaoutput.csv: VARIABLE: NAMES = y a1a8 z W1W3 MLC; USEVARIABLES = y z W1W3 ; CLASSES = c(3); Training = W1W3(bch); ANALYSIS: TYPE = MIXTURE; MODEL: %Overall% C y on z; %c#1% y on z; %c#2% y on z; %c#3% y on z; Q1: How to I modify the second input file to obtain what I described above? Q2: How do I test for statistically significant differences in the classspecific intercepts? Thank you! 


Q1: Try dropping the classspecific y on z statements. The intercept varies by class as the default. Q2: Give parameter labels to the classspecific intercepts in the Model command, like: %c#1% [y] (p1); %c#2% [y] (p2); %c#3% [y] (p3); and then use Model Test to do Wald testing like testing if all 3 are the same: 0 = p1p2; 0 = p3p1; 

Paul Spin posted on Wednesday, December 20, 2017  11:22 am



Thank you. I am running my analysis across various multiply imputed datasets. Is there a way to ensure class order stability across each dataset? In other words, I would like C=1 to denote class category 1 "No asthma" in each iteration. 

Paul Spin posted on Wednesday, December 20, 2017  12:23 pm



I should add that removing the classspecific yonz's does not stop the program from estimating classspecific coefficients, which seems to imply that the MODEL TEST part is adjusted for main effects and classspecific interaction effects. I'd rather not condition on the latter. Here is a snippet of my output file without the classspecific statements: MODEL RESULTS TwoTailed Estimate S.E. Est./S.E. PValue Latent Class 1 ABSENT ON GRADE_2 0.040 0.152 0.264 0.792 TARDIES ON GRADE_2 0.070 0.224 0.311 0.756 Latent Class 2 ABSENT ON GRADE_2 0.040 0.152 0.264 0.792 TARDIES ON GRADE_2 0.070 0.224 0.311 0.756 So, my followup questions are: 1) Does MPLUS estimate these coefficients using one large model with interactions or separate classspecific models. 2) Can I get what I want simply by adding grade_2@0 to the all but the first classspecific specification, i.e. imposing the assumption that there are no classspecific deviations in coefficients relative to the base class? 


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